Method and system for crop yield estimation

ABSTRACT

A method for identifying the presence of fruit in image data in an image sensor of a scene includes acquiring image data in an image sensor for at least two distinct wavelengths of a scene. A normalized difference reflectivity index (NDRI) for each location in an array of locations in the image data is calculated with respect to said at least two distinct wavelengths. Regions in the array of locations are identified where the value of the calculated NDRI of the locations in these regions is within a range of values indicative of a presence of fruits in the scene. An output is generated on an output device with information related to the identified presence of fruits.

RELATED APPLICATIONS

This application is a Continuation of U.S. patent application Ser. No.15/353,754, having a filing date of Nov. 17, 2016. The content of theabove application is incorporated by reference as if fully set forthherein in its entirety.

FIELD OF THE INVENTION

The present invention relates to crop yield estimation. Moreparticularly, the present invention relates to methods and systems foridentifying fruit in an image.

BACKGROUND OF THE INVENTION

Crop yield estimation may be used for determining investment costs andanticipated revenue in managing an orchard. Cost estimates for waterexpenditure, application and quantity of fertilizer to be applied tofruit trees, and manual labor costs needed for caring for the fruittrees from the growing season and until harvesting the ripe fruit fromthe fruit trees, for example, are based on fruit yield estimates. Anyerrors in fruit yield estimates may be very costly.

Methods based on human inspection to count a number of fruits on a treemay be time-consuming and inefficient. For example, determining a yieldof fruit from a tree may be computed by defining an area of a fruittree, for example, by using a metal frame and counting the fruits withinthe area defined by the metal frame. Counting the fruit in this mannerin one or two trees in a hectare, for example, may be used to estimatethe fruit yield in the orchard. Additionally, RGB photo snapshots of thetrees may be taken and the fruits of each tree in the acquiredphotographs may be counted and the fruit count used to estimate fruityield.

It is desirable to have a system and method for accurately andautomatically counting fruit in fruit trees and mapping the number offruit in the fruit trees over a geographical region, such as an orchard,for example.

SUMMARY OF THE INVENTION

There is thus provided, in accordance with some embodiments of thepresent invention, a method for identifying the presence of fruit inimage data in an image sensor of a scene including acquiring image datain an image sensor for at least two distinct wavelengths of a scene. Anormalized difference reflectivity index (NDRI) is calculated for eachlocation in an array of locations in the image data with respect to saidat least two distinct wavelengths. Regions in the array of locations areidentified where the value of the calculated NDRI of the locations inthese regions is within a range of values indicative of a presence offruits in the scene. An output on an output device is generated withinformation related to the identified presence of fruits.

In accordance with some embodiments of the present invention, the sceneincludes a plant or a part of a plant.

In accordance with some embodiments of the present invention, theinformation includes an estimate of a number of fruits identified in thescene.

In accordance with some embodiments of the present invention, the sceneincludes a part of a plant, and estimating a number of fruits in thepart of the plant includes using the information with the estimate ofthe number of fruits identified in the scene and applying a statisticalmodel to estimate a number of hidden fruits not identified in the scene.

In accordance with some embodiments of the present invention, the methodincludes estimating a distribution of fruit sizes in the scene using adeep learning module.

In accordance with some embodiments of the present invention, the imagesensor is mounted on a vehicle configured to move through thegeographical region, and the method includes acquiring image data of aplurality of scenes of the geographical region with the image sensor.

In accordance with some embodiments of the present invention, theplurality of scenes includes a plurality of images of fruit trees in thegeographical region, and the method includes using the information fromthe image data of the plurality of scenes so as to estimate a number offruits in the fruit trees in the geographical region.

In accordance with some embodiments of the present invention, the methodincludes identifying locations of the fruit trees in the geographicregion using global positioning system (GPS) data.

In accordance with some embodiments of the present invention, the methodincludes estimating a number of fruits in the identified regions whichinclude an overlap between two or more fruits in a fruit cluster using adeep learning module.

In accordance with some embodiments of the present invention, thegenerated output with information related to the identified presence offruits is stored in a storage device on a remote server.

There is further provided, in accordance with some embodiments of thepresent invention, a system for identifying the presence of fruit inimage data of a scene comprising an image sensor and a processor. Theimage sensor is configured to acquire image data for at least twodistinct wavelengths of a scene. The processor is configured tocalculate a normalized difference reflectivity index (NDRI) for eachlocation in an array of locations in the image data with respect to saidat least two distinct wavelengths, to identify regions in the array oflocations where the value of the calculated NDRI of the locations inthese regions is within a range of values indicative of a presence offruits in the scene, and to generate an output on an output device withinformation related to the identified presence of fruits.

In accordance with some embodiments of the present invention, the imagesensor includes a beamsplitter configured to split light concurrentlyonto at least two arrays of light sensors in the image sensor, each ofthe at least two arrays sensitive to light at each of the at least twodistinct wavelengths, so as to acquire image data of the scene at the atleast two distinct wavelengths.

BRIEF DESCRIPTION OF THE DRAWINGS

In order for the present invention, to be better understood and for itspractical applications to be appreciated, the following figures areprovided and referenced hereafter. It should be noted that the figuresare given as examples only and in no way limit the scope of theinvention. Like components are denoted by like reference numerals.

FIG. 1 schematically illustrates a system for crop estimation mounted ona tractor for scanning a geographical region of plants with fruit, inaccordance with some embodiments of the present invention;

FIG. 2 schematically illustrates a system for crop estimation, inaccordance with some embodiments of the present invention;

FIG. 3 schematically illustrates reflectivity data of citrus fruit andleaves as a function of wavelength, in accordance with some embodimentsof the present invention;

FIG. 4 is a graph of normalized difference reflectivity index (NDRI)values for distinguishing between fruits and leaves in acquired imagedata based on the reflectivity, in accordance with some embodiments ofthe present invention;

FIG. 5 schematically illustrates a hand-held system for crop estimation,in accordance with some embodiments of the present invention;

FIG. 6 schematically illustrates a system for crop estimationcooperating with a remote device, in accordance with some embodiments ofthe present invention; and

FIG. 7 is a flowchart depicting a method for crop estimation, inaccordance with some embodiments of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

In the following detailed description, numerous specific details are setforth in order to provide a thorough understanding of the invention.However, it will be understood by those of ordinary skill in the artthat the invention may be practiced without these specific details. Inother instances, well-known methods, procedures, components, modules,units and/or circuits have not been described in detail so as not toobscure the invention.

Although embodiments of the invention are not limited in this regard,discussions utilizing terms such as, for example, “processing,”“computing,” “calculating,” “determining,” “establishing”, “analyzing”,“checking”, or the like, may refer to operation(s) and/or process(es) ofa computer, a computing platform, a computing system, or otherelectronic computing device, that manipulates and/or transforms datarepresented as physical (e.g., electronic) quantities within thecomputer's registers and/or memories into other data similarlyrepresented as physical quantities within the computer's registersand/or memories or other information non-transitory storage medium(e.g., a memory) that may store instructions to perform operationsand/or processes. Although embodiments of the invention are not limitedin this regard, the terms “plurality” and “a plurality” as used hereinmay include, for example, “multiple” or “two or more”. The terms“plurality” or “a plurality” may be used throughout the specification todescribe two or more components, devices, elements, units, parameters,or the like. Unless explicitly stated, the method embodiments describedherein are not constrained to a particular order or sequence.Additionally, some of the described method embodiments or elementsthereof can occur or be performed simultaneously, at the same point intime, or concurrently. Unless otherwise indicated, us of the conjunction“or” as used herein is to be understood as inclusive (any or all of thestated options).

Some embodiments of the present invention herein describe methods andsystems for crop estimation, such as estimating a number of fruits,and/or a fruit load, and/or a fruit yield from a crop of plants locatedwithin a geographical region from image data of a plurality of scenesacquired in an image sensor. The scene may include, for example,pictures of fruit trees, or parts of fruit trees, such as citrus fruittrees, planted in the geographical location, typically an orchard. Theimage sensors, typically a camera, for example, may be coupled to animage processing unit where the image data may be processed to identifyfruits on a fruit tree in the captured image of the fruit tree in thescene, for example. Thus, image data of a plurality of scenes includingfruit trees in the orchard may be used to estimate the number of fruitsor fruit yield in the orchard using the methods and systems describedherein below. Near infrared wavelengths may be used to distinguishbetween green fruits (e.g., before ripening) and green leaves.

FIG. 1 schematically illustrates a system for crop estimation 30 mountedon a tractor 20 for scanning a geographical region 10 of plants 15 withfruit 17, in accordance with some embodiments of the present invention.Geographical region 10 may include, for example, an orchard and/or avineyard. Plants 15 may include fruit trees 15 with fruit 17. Fruit 17on fruit trees 15 may include fruits, vegetables, and/or nuts, forexample. Plants 15 may include all types of fruit trees, such as citrusfruit trees, decumbent plants, such as vines, and fruits of deciduoustrees. Fruits growing on decumbent plants may include, for example,tomatoes and squash, as well as melons and watermelons. Fruit 17 mayalso include olives and sub-tropical fruits, such as avocado, mango,etc.

Images sensors 35 and 40, such as cameras for example, in system forcrop estimation 30 may be coupled to an image processing unit (IPU) 45.System 30 may be mounted on a vehicle, such as tractor 20. Tractor 20may move through orchard 10. A first camera 35 and a second camera 40mounted on the front and/or back of tractor 20 may be used to acquireimages of fruit trees 15, bushes, plants, etc. (hereinafter—“trees” forbrevity) in orchard 10, grove, plot, field etc. (hereinafter—“orchard”for brevity) of all or a portion of the fruit trees in the orchard. Inthe embodiment shown in FIG. 1, as the tractor moves between two rows oftrees 15 in a scanning session, first camera 35 may be oriented toacquire images of one row of fruit trees and second camera 40 the otherrow of trees 15, for example, in two opposite rows of trees.

Each camera may acquire image data of a scene 32 shown in an inset 43 ofFIG. 1. Scene 32 may include all or part of fruit tree 15 with fruit 17as shown in inset 43. The image data in scene 32 may be partitioned intoan array of locations 19. The array of locations 19 may be characterizedas having any suitable resolution, e.g., based on the image sensorspecification. In some embodiments, each of locations 19 in the arraymay correspond to a single image pixel. In other embodiments, each oflocations 19 in the array may correspond to a cluster of pixels. Aregion 22 may include multiple locations in the array of locations 19where IPU 45 identifies a presence of fruits in the region.

Image processing unit 45 may be configured to estimate a number of fruit17 in scene 32. First 35 and second 40 cameras may acquire a pluralityof images of scenes 32 for all or a portion of fruit trees 15 in orchard10. Image processing unit 45 may be configured to estimate a number offruits from the plurality of images from the number of fruits visible inthe image data using spectral remote sensing methods. Statisticalprocessing may then be used to estimate a number of fruits on the treenot imaged in the plurality of fruit tree images in scenes 32. Forexample, the image data may include pictures of fruits across a surfaceof the tree. Leaves and fruits on the surface may block the view offruits hidden behind the surface of scene 32. IPU 45 may estimate thenumber of fruits identified (e.g., visible) in the image data in scene32 and apply statistical data (e.g., statistical modeling) to estimatethe hidden fruit not identified in scene 32 so as to estimate the totalnumber of fruits in scene 32.

In some embodiments, IPU 45 may concatenate and/or superimpose imagedata acquired in a plurality of scenes including parts of the same treeor a plurality of trees so as to obtain an estimate of the number offruits visible in the image data of a single fruit tree or a group oftrees. The estimate of the number of fruit on a fruit tree or a group oftrees may include the number of fruit that were identified in theprocessed image data and an estimated addition of fruits based on astatistical analysis (e.g., extrapolation) to account for the hiddenfruit on the fruit tree or trees not visible in the image data.

Image processing unit 45 may cooperate with a global positioning system(GPS) and inertial navigation system (INS) with a GPS/INS satellite 25,which may relay the position of the tractor and the image sensors inorchard 10 to IPU 45. The location or position the acquired image datafor each scene of the plurality of fruit trees 15 may be computed fromthe relayed GPS/INS data when the image data is acquired using theposition of the tractor relative to each of the fruit trees, forexample, during the scanning session.

Using statistical models and the image data of scenes including aplurality of fruits trees in orchard 10, IPU 45 may estimate a number offruits in the orchard and/or a fruit yield such as the number of fruitsper a given area of the orchard (e.g., hectare), for example. Using theGPS data to locate positions of the fruit trees in the orchard, IPU 45may be used to map the density of fruits in the fruit trees withingeographical area, or orchard 10.

FIG. 2 schematically illustrates a system 50 for crop estimation, inaccordance with some embodiments of the present invention. System 50 mayinclude an imaging unit 31 which may communicate with a server 110 viathe internet 100. Imaging unit 31 may include first image sensor 35,second image sensor 40 and a laser unit 37 which may be coupled to imageprocessing unit (IPU) 45 via a first image sensor interface 90, a secondimage sensor interface 93 and a laser interface 95. Although theembodiments shown in FIGS. 1 and 2 illustrate two image sensors, anynumber of image sensors (e.g., at least one image sensor) may be used.Laser unit 37 may be used as a laser distance sensor to determine thedistance between cameras (e.g., image sensors 35 and 40) mounted onvehicle 20 and trees 15 as shown in FIG. 1. IPU 45 may include aprocessor 55 coupled to a memory 80 and a storage device 83. Processor55 may include one or more processing units, e.g. of one or morecomputers.

IPU 45 may include control circuitry 75, a communication interface 70for communicating with a remote server 110 (e.g., used for cloudcomputing) via the internet 100 (e.g., over a TCP/IP connection), and aGPS/INS communication unit 65. GPS/INS communication unit 65 may becoupled to an antenna 60 and may be used to communicate with and receivelocation and position data of IPU 45 (e.g., fruit trees in the orchard)with a global positioning system (GPS) and an inertial navigation system(INS) using GPS/INS satellite 25.

Processor 55 may be configured to communicate with an input/outputterminal 160 including an input device 165 and an output device 170 viaan input/output interface 85. Input/output terminal 160 may include acomputer.

Processor 55 may be configured to communicate with input device 165. Forexample, input device 165 may include one or more of a keyboard, keypad,or pointing device for enabling a user to inputting data or instructionsfor operation of processor 55.

For example, output device 170 may include a computer monitor or screen.Processor 55 may be configured to communicate with a screen of outputdevice 170 to output information related to the identified presence offruits in image data of scenes of plants, such as fruit trees, withfruit. In another example, output device 170 may include a printer,display panel, speaker, or another device capable of producing visible,audible, or tactile output.

Processor 55 may be configured to communicate with memory 80. Memory 80may include one or more volatile or nonvolatile memory devices. Memory80 may be utilized to store, for example, programmed instructions foroperation of processor 55, data or parameters for use by processor 55during operation, or results of operation of processor 55

Processor 55 may be configured to communicate with data storage device83. Data storage device 83 may include one or more fixed or removablenonvolatile data storage devices. For example, data storage device 83may include a computer readable medium for storing program instructionsfor operation of processor 55. In this example, the programmedinstructions may take the form of image processing routines and/orinstructions in a spectral processing module 52, a deep learning module54, a statistical processing module 56, and a proximity processingmodule 58 for processing the image data of scenes in the image sensors.Data storage device 83 may be utilized to store data or parameters foruse by processor 55 during operation, or results of operation ofprocessor 55.

IPU 45 may be configured to communicate with remote server 110 over theinternet 100 using a wired connection and/or by wireless communicationby using any suitable wireless communication standard such MobileCommunications (GSM), General Packet Radio Service (GPRS), High SpeedPacket Access (HSPA), Enhanced Data Rates for Global Evolution (EDGE),Long Term Evolution (LTE), Code Division Multiple Access (CDMA),Wireless Fidelity (WiFi), and/or Bluetooth.

In some embodiments of the present invention, imaging unit 31 mayinclude all of the components and image processing features so as tocompute the parameters for crop yield estimation in a stand-aloneimaging unit 30 as shown in FIG. 1. For example after a scanningsession, stand-alone imaging unit 30 may be connected to input/outputterminal 160 to receive the crop estimation data and reports from theimage data and/or GPS data collected during the scanning session oforchard 10, which may be stored in memory and/or storage 83.Alternatively and/or additionally, cloud computing may be used whereimaging unit 31 acquires the image data of the scenes using first 35 andsecond 40 image sensors and relays the image data to remote server 110.Remote server 110 may use cloud computing to manage to the image dataacquired from the images sensors and to estimate the number of fruit inthe acquired images.

Remote server 110 may include server circuitry 120 and one or moreprocessors 125, which may communicate with a server storage device 132and a server memory 130. Server 110 may include a communicationinterface 135 for communicating with IPU 45 via the internet, forexample. Remote server 110 may include an antenna 115 to communicatewith GPS/INS satellite 25 to receive the location of plants such asfruit tree 15 in geographical area 10.

Server storage device 132 may be used to store the acquired image data,processed image data, and the programming instructions used to implementthe image data processing flow including spectral processing module 52,deep learning module 54, statistical processing module 56, and proximityprocessing model 58 in the form of an installation package or packagesthat can be downloaded and installed for execution by a server processor125. Server storage device 132 may include a database such as for theacquired/processed image data and location data, for example.

In some embodiments of the present invention, memories 80 and 130 mayinclude data storage devices 83 and 132, respectively, such asnonvolatile memory, or flash memory.

In some embodiments of the present invention, a user 141 of system 10may receive information about the presence of fruit in the orchard viainternet 100 in a communication device 140, such as a cellphone and/orhandheld tablet, for example. Communication device 140 may communicatewith remote server 110 and/or IPU 45 either directly with a wiredconnection and/or over the internet 100 using any suitable wirelesscommunication standard such as Global System for Mobile Communications(GSM), General Packet Radio Service (GPRS), High Speed Packet Access(HSPA), Enhanced Data Rates for Global Evolution (EDGE), Long TermEvolution (LTE), Code Division Multiple Access (CDMA), Wireless Fidelity(WiFi), and/or Bluetooth. Communication device 140 may include an imagesensor such as a camera for acquiring image data of the fruit trees inthe orchard, for example.

Early seasonal citrus fruits may be the same color as the green leavesbetween the time of about one month after the citrus fruit trees flowersto just before the fruits ripen into a yellow and/or orange color, forexample. It may be difficult to differentiate between the early greenfruit and the green leaves at this stage of growth by use of fruit treeimages acquired by image data using RGB cameras, for example.

In some embodiments of the present invention, spectral remote sensingmethods using near infrared (NIR) wavelengths of the spectrum may beused to distinguish between the green fruits and the green leaves whichmay be used to estimate the number of fruits on the fruit trees. Light,such as sunlight or an artificial source of light, for example,impinging on the tree may reflect off different parts of the treeincluding the leaves and the fruit into the camera. The reflected lightmay be acquired as image data of scene including a fruit tree, or partof a fruit tree in the image sensor, such as a camera. Fruits and leavesmay exhibit different spectral properties at certain NIR wavelengths.

The methods described herein to identify the presence of citrus fruitsin the image data of a scene acquired by an image sensor are not by wayof limitation of the embodiments of the present invention. Any fruits,vegetables and/or nuts may be identified using these methods, such asthe fruits of deciduous trees, sub-tropical fruits (e.g., avocado,mango), olives, etc.

In some embodiments of the present invention, image sensors 35 and 40may include a camera, such as a monochromatic camera. Such monochromaticcameras may include a plurality of photo-sites, or light sensors,arranged in a spatial array. Each light sensor may produce an electricalsignal proportional to the light intensity or light power impinging onthe active area of the light sensor for a given period of time (e.g.,integration time). However, the monochromatic light sensors aretypically sensitive to all visible and NIR wavelengths impinging on theactive area of the light sensors. In some embodiments, filters may beused to differentiate between the at least two wavelengths.

In some embodiments of the present invention, at least two NIR filterswith a passband frequencies corresponding to the at least twowavelengths may be mounted on a rotating wheel above the camera lens.Image data may be acquired, for example, as the rotating wheel rotatesand places a filter with one passband wavelength above the lens to takeimage data at one passband frequency. Then a different filter withanother passband frequency may be rotated into place above the lens totake image data of the same scene, and so forth. Image data for the atleast two wavelengths may be used to differentiate between the greenfruit and the green leaves.

In this case where each of the plurality of locations 19 in the arraysuch as an X-Y array represents an image pixel, for example, each pixelmay include a pixel value related to the illumination power captured bythe respective light sensor at a given wavelength set by the filters.The pixel value may be related to the intensity of light reflected fromthe fruit tree at the given wavelength and acquired by the light sensorassociated with the pixel. The reflectivity at the given wavelength ateach pixel may be determined from the pixel value. In some embodiments,flat field corrections may be used to remove the electronic and photonicnoise from the image data so as to obtain the reflectance at each pixelin the image data.

Each location (e.g., in this case, each pixel) in the array may berepresented as a spectral cube with two spatial dimensions such as theX-Y dimensions, for example, and one spectral dimension, e.g., one ofthe at least two wavelengths. If three wavelengths are used, each pixelor spectral cube may be spatially defined with X-Y dimensions of thepixel and may include three spectral layers corresponding to the threewavelengths.

In some embodiments of the present invention, image sensors 35 and 40may include a color camera. However, each light sensor in the camera mayinclude multiple NIR filters such that each location 19 from theplurality of locations may include one or more pixels, where each pixelfrom the one or more pixels are sensitive to one wavelength from the atleast two wavelengths as determined by the respective NIR filter.

In some embodiments of the present invention, image sensors 35 and 40,such as a camera, may include a beamsplitter configured to split lightconcurrently onto at least two arrays of light sensors in the imagesensor, each of the at least two arrays sensitive to light at each ofthe at least two distinct wavelengths, so as to acquire image data ofthe scene for the at least two distinct wavelengths.

In some embodiments, two or more respective light beams generated by thebeamsplitter may pass through internal filters in the image sensor withpassband frequencies corresponding to the at least two distinctwavelengths for selectively filtering the light beams for exciting eachof the at least two arrays of light sensors. In some embodiments, awavelength selective beamsplitter, such as an optical prism or anoptical grating, may be used to selectively split the light into atleast two light beams at each of the at least two wavelengths forexciting each of the at least two arrays of light sensors. Additionalfiltering may also be applied to the light beams after the prism.

In some embodiments of the present invention, a camera enhanced to NIRspectral wavelengths (e.g., about 1100 nm) including three filters withrespective passband responses at 810, 835, and 970 nm, for example, maybe used to acquire image data of the scene with parts of the citrus treeand/or the entire citrus tree. The spatial resolution of the camera maybe fixed in accordance with the target (e.g., citrus fruit) spatialrequirements. For example, the size of the green citrus fruits at thebeginning of the growing season may be 20 mm. For this case, the spatialresolution may be 2 mm, for example, to capture and differentiatebetween the fruit and leaves.

FIG. 3 schematically illustrates reflectivity data 200 of citrus fruit205 and leaves 210 as a function of wavelength, in accordance with someembodiments of the present invention. The reflectivity data shown inFIG. 3 was acquired using a portable spectrometer to analyze lightreflected from the citrus fruit and green leaves of the citrus fruittree, such as, for example, ASD FieldSpec Spectroradiometers, AnalyticSpectral Devices (ASD), Inc., Boulder, Col., U.S.A. The difference inreflectivity values may be used as a spectral ‘fingerprint’ todifferentiate between fruit and leaves on the citrus fruits on the fruittree in the acquired image data. In some embodiments, image sensors 35and 40 may be used to acquire image data of scene 32 with an entirecitrus fruit tree or a part thereof using three wavelengths of NIR lightat 810, 835, and 970 nm.

A first marker 215 may intersect the reflectivity curves at 810 nm. Asecond marker 220 may intersect the reflectivity curves at 835 nm. Athird marker 225 may intersect the reflectivity curves at 970 nm. Forexample, FIG. 3 illustrates that a fruit may have reflectivity values of0.84 at 810 nm, 0.83 at 835 nm, and 0.61 at 970 nm. Note that, at 970nm, the reflectivity for citrus fruit is about 20% lower than thereflectivity at 810 and 835 nm, and this characteristic may be used toidentify citrus fruit in location 19. Similarly, leaves may havereflectivity values of 0.71 at 810 nm, 0.72 at 835 nm, and 0.70 at 970nm. In some embodiments, citrus fruit reflectivity curve 205 may exhibita full width half minimum (FWHM) of 30 nm at third marker 225 at 970 nmintersecting the minimum of citrus fruit reflectivity curve 205 as shownin FIG. 3.

In some embodiments, the image sensor, or monochromatic camera withthree filters at 810, 835, and 970 nm may be used to acquire image datawith the image pixel values at each of the three wavelengths at 810,835, and 970 nm for each location 19, or pixel. The image pixel valuesat each of the three wavelengths at 810, 835, and 970 nm may be used toobtain the reflectivity at each of the three wavelengths at each pixel.Location 19 may include one pixel, or may include multiple pixels.Nevertheless, the reflectivity values at each location 19 may bedetermined and may be used as an indication of fruit at location 19 inthe image data of scene 32.

In some embodiments of the present invention, a normalized differencereflectivity index (NDRI) for each location 19 in an array of locationsin the image data may be calculated for pairs of wavelengths chosen fromthe at least two distinct wavelengths (e.g., 810, 830, and 970 nm, inthis example). NDRI may be defined in equation (1) by

NDRI(R _(λ1) , R _(μ2))=(R _(λ1) −R _(λ2))/(R _(λ1) +R _(λ2))  (1)

where R_(λ1) and R_(λ2) are the reflectivity values at λ₁ and λ₂,respectively.

In the case of citrus fruits as shown in FIG. 3, the NDRI value at eachlocation 19 in scene 32 may be computed using the reflectivity data forcitrus fruit and leaves from FIG. 3. NDRI (810 nm, 970 nm) may be on theorder of 0.16 for fruit and 0.01 for leaves. Similarly, NDRI (835 nm,970 nm) may be on the order of 0.15 for fruit and 0.01 for leaves.Although each value of NDRI (810 nm, 970 nm) and NDRI (835 nm, 970 nm)parameters alone may be used to determine whether fruit or leaves arepresent in location 19, both NDRI parameters may be used together toincrease the probability of correctly identifying fruit in location 19.

In some embodiments of the present invention, the identifying regions,e.g., region 22 in the array of locations 19 where the value of thecalculated NDRI for each location 19 is within a range of values thatmay be indicative of a presence of fruits in scene 32. The range ofvalues indicative of the presence of fruits may be determined from thegraph shown in FIG. 4. These ranges may then be used to distinguishbetween the target (e.g., green fruit) and the background (e.g., greenfoliage, such as the green leaves of the fruit tree) at each location 19in the array.

FIG. 4 is a graph 230 of normalized difference reflectivity index (NDRI)values for distinguishing between fruits 235 and leaves 240 in acquiredimage data based on the reflectivity, in accordance with someembodiments of the present invention. FIG. 4 shows NDRI valuescalculated for multiple samples of green citrus fruit and green leaves,denoted by the target (e.g., citrus fruits) and the background (e.g.,leaves), which may be applied to the image data at each location 19. Aplot of NDRI (835 nm, 970 nm) vs NDRI (810 nm, 970 nm) also known as thespectral band ratios as shown in FIG. 4 illustrates that a range ofvalues where NDRI (810 nm, 970 nm) and NDRI (835 nm, 970 nm) are lessthan 0.06 may indicate that leaves have been imaged in scene 32.Similarly, a range of values where NDRI (810 nm, 970 nm) and NDRI (835nm, 970 nm) are greater than 0.13 may indicate that fruits have beenimaged in scene 32.

In some embodiments of the present invention, the inputs to IPU 45 mayinclude the spectral images of scene 32 (e.g., the image data for the atleast two wavelengths) acquired by image sensors 35 and 40. The spectralimages may be processed by a spectral processing flow including spectralprocessing module 52 and deep learning module 54 as shown in FIG. 2, anda location analysis processing flow including statistical processingmodule 56 and proximity processing module 58. The spectral images may belocally processed by processor 55 in IPU 45, or by processor 125 inremote server 110.

In some embodiments of the present invention, image sensors 35 and 40(e.g., monochromatic camera) may be configured to acquire the data whichmay include image pixel values indicative the radiance of light incidenton the tree, such as sunlight or an artificial source of light, andreflected into the camera. The radiance, or radiative flux, detected bythe sensors 35 and 40 may be used by IPU 45 and/or remote server 110 incompute NRDI values at each location 19.

In some embodiments of the present invention, spectral processing module52 may use the image data acquired by the camera to determine thereflectance values at each pixel in the following manner. First, thedigital number (DN), or pixel value, at each pixel from the lightsensors in the camera are converted into energy values, or radiancevalues. Radiometric calibration information typically supplied by thecamera manufacturer may be used to normalize the radiance values toreflectance values at each pixel. Spectral processing module 52 may beused to compute the normalized difference reflectivity index (NDRI) ateach location 19 in the array of locations 19 in the image data.

The computed spectral band ratios (e.g., NDRI at different pairs ofwavelengths) may then be used to distinguish between the fruit (e.g.,target) and the leaves (e.g., background) in the scene. Spectralprocessing module 52 may apply image geometric corrections to the imagedata due to distortions that may occur in using a camera with a wideangle lens to image the fruit tree. The distance between the camera andthe fruit tree may be determined using laser unit 37 and used to correctwide angle lens distortion.

In some embodiments of the present invention, deep learning module 54,or machine learning module, may be used to identify a number of fruitsin scene 32 once fruits are indicated in location 19 from the NDRIvalue. For example, a citrus fruit may have a circular shape, but may becovered partially by leaves resulting in a detection of fruit in avariety of odd-shapes, or fruit fragments, in the array of imagelocations 19 in the image data. Additionally, a region 22 with indicatedfruit may include an overlap between two or more fruits in a fruitcluster. Thus, deep learning module 54 may be used to estimate one ormore fruits in the scene from a variety of shapes detected in locations19 with fruit. In some embodiments, deep learning module 54 may be usedto estimate a fruit size, for example, based on a curve in a detectedfruit fragment. Deep learning module 54 may be also used to estimate adistribution of fruit sizes of fruits identified in the scene.

In some embodiments of the present invention, statistical processingmodule 56 may be used to estimate a number of fruits on the tree notcaptured the plurality of fruit tree images in scenes 32 as previouslydescribed. The image data may include pictures of fruits on the outercontour of the tree while leaves and fruits on the outer contour mayblock imaging fruits hidden behind the outer contour of scene 32.Statistical data from a variety of trees in different geographiclocations, for example, may be used to construct models used bystatistical processing module 56 to estimate a number of fruits on thetree not captured in the fruit tree images (e.g., behind the fruit andleaves on the outer contour of the fruit tree).

In some embodiments of the present invention, proximity processingmodule 58 may be used to obtain the exact location and position of eachimage acquired during an orchard scan, for example. Proximity processingmodule 58 may receive inputs from GPS/INS communication unit 65 as shownin FIG. 2. In some embodiments, the location and position data may berecorded during the acquisition of the image of each fruit tree (or partof a fruit tree) imaged during the scanning session. The location andposition data may be used to calculate a fruit yield or fruit densityper hectare, for example, as well as to map the density of fruits in theorchard upon estimating the number of fruits in the orchard.

In some embodiments of the present invention, the data from spectralprocessing flow and the location analysis processing flow may be used togenerate a geo-database (GeoDB) that may be stored, for example, incloud storage, such as server storage device 132 in remote server 110based on the acquired and processed image data, and location data basedon the geographical location of geographic region 10. The database mayalso include data taken over many years and/or cycles of planting andharvesting crops in the same and/or different geographical regions. Forexample, citrus fruits planted in Europe may have different crop yieldsthan the same citrus fruits planted in Florida, for example. Outputreports extracted from the GeoDB may be generated by system 50 in theform of a thematic map, written and/or tabular reports which may be sentto a user of the GeoDB.

FIG. 5 schematically illustrates a hand-held system 300 for cropestimation, in accordance with some embodiments of the presentinvention. A user 305, such as a farmer, may acquire image data of afruit tree 315 with fruits 317 using a hand-held device 310 including animage sensor, such as a camera. Hand-held device 310 may be configuredto send 320 the image data of fruit tree 315 over the internet 100(e.g., by wireless, cellular or wired connection) to a remote cloudserver 110 (e.g., in FIG. 2) which receives and processes the imagedata. User 305 may receive 325 an estimate of a number of fruits 317 ontree 315 on hand-held device 310 from the remote server in the cloud.

FIG. 6 schematically illustrates a system 350 for crop estimationcooperating with a remote device 390, in accordance with someembodiments of the present invention. A tractor 355 may acquire imagesof fruit trees 365 and fruit trees 375 in a scanning session coveringall or a portion of the fruit trees in the orchard. A first camera 360may be used to acquire image data of trees 365 with fruit 362 to theleft side of tractor 355, for example, and a second camera 370 may beused to acquire image data of trees 375 with fruit 362 to the right sideof tractor 355 as shown in FIG. 6. First camera 360 and second camera370 may be configured to send 380 the image data over the internet 100(e.g., by wireless, cellular or wired connection) to a remote cloudserver 110 (e.g., in FIG. 2) which receives and processes the imagedata. Although the cameras are shown in FIG. 6 mounted on the rear oftractor 355, the cameras may be mounted in any suitable location ontractor 355, such as on top of the tractor frame, for example. A user395 may receive 385 a report on remote communication device 390, such asa tablet, for example, with data about the number of fruits in the fruittree in the orchard, as well as other relevant data such as location,mapping of the fruits in the orchard and fruit yield.

FIG. 7 is a flowchart depicting a method 400 for crop estimation, inaccordance with some embodiments of the present invention. In theexample of FIG. 7, method 400 may be executed by processor 55 of imageprocessing unit 45 and/or in processor 55 of server 110. Method 400 maybe executed upon a request or command that is issued by a user, orautomatically issued by another application.

Method 400 may include acquiring 410 image data in an image sensor forat least two distinct wavelengths of a scene. Method 400 may includecalculating 415 a normalized difference reflectivity index (NDRI) foreach location in an array of locations in the image data with respect tothe at least two distinct wavelengths. Method 400 may includeidentifying 420 regions in the array of locations where the value of thecalculated NDRI of the locations in these regions is within a range ofvalues indicative of a presence of fruits in the scene. Method 40 mayinclude generating 425 an output on an output device with informationrelated to the identified presence of fruits.

It should be understood with respect to any flowchart referenced hereinthat the division of the illustrated method into discrete operationsrepresented by blocks of the flowchart has been selected for convenienceand clarity only. Alternative division of the illustrated method intodiscrete operations is possible with equivalent results. Suchalternative division of the illustrated method into discrete operationsshould be understood as representing other embodiments of theillustrated method.

Similarly, it should be understood that, unless indicated otherwise, theillustrated order of execution of the operations represented by blocksof any flowchart referenced herein has been selected for convenience andclarity only. Operations of the illustrated method may be executed in analternative order, or concurrently, with equivalent results. Suchreordering of operations of the illustrated method should be understoodas representing other embodiments of the illustrated method.

Different embodiments are disclosed herein. Features of certainembodiments may be combined with features of other embodiments; thuscertain embodiments may be combinations of features of multipleembodiments. The foregoing description of the embodiments of theinvention has been presented for the purposes of illustration anddescription. It is not intended to be exhaustive or to limit theinvention to the precise form disclosed. It should be appreciated bypersons skilled in the art that many modifications, variations,substitutions, changes, and equivalents are possible in light of theabove teaching. It is, therefore, to be understood that the appendedclaims are intended to cover all such modifications and changes as fallwithin the true spirit of the invention.

While certain features of the invention have been illustrated anddescribed herein, many modifications, substitutions, changes, andequivalents will now occur to those of ordinary skill in the art. It is,therefore, to be understood that the appended claims are intended tocover all such modifications and changes as fall within the true spiritof the invention.

What is claimed is:
 1. A system for identifying the presence of fruit inimage data of a scene, the system comprising: a processor configured to:receive image data at least two distinct wavelengths of a scene, whereinsaid at least two distinct wavelengths comprise (a) 970 nanometers(nm)±15 nm, and (b) 810 nm±15 nm or 835 nm±15 nm; and calculate anormalized difference reflectivity index (NDRI) for each location in anarray of locations in the image data with respect to said at least twodistinct wavelengths, to identify regions in the array of locationswhere the value of the calculated NDRI of the locations in these regionsis within a range of values indicative of a presence of fruits in thescene, and generate an output on an output device with informationrelated to the identified presence of fruits.
 2. The system according toclaim 1, wherein the system comprises at least one image sensorconfigured to acquire said image data of a scene at said at least twodistinct wavelengths.
 3. The system according to claim 2, wherein theimage sensor comprises a beam splitter configured to split lightconcurrently onto at least two arrays of light sensors in the imagesensor, each of the at least two arrays sensitive to light at each ofthe at least two distinct wavelengths, so as to acquire image data ofthe scene at the at least two distinct wavelengths.
 4. The systemaccording to claim 2, wherein the image sensor comprises at least twobandpass filters with passband frequencies corresponding to the at leasttwo distinct wavelengths.
 5. The system according to claim 2, furthercomprising a vehicle configured to move through a geographical regionwith the image sensor mounted on the vehicle, and wherein the imagesensor is configured to acquire image data of a plurality of scenes ofthe geographical region.
 6. The system according to claim 5, whereinplurality of scenes comprises a plurality of images of fruit trees inthe geographical region and wherein the processor is configured to usethe information from the image data of the plurality of scenes so as toestimate a number of fruits in the fruit trees in the geographicalregion.
 7. The system according to claim 6, further comprising a globalpositioning system (GPS) unit communication unit, and wherein theprocessor is configured to identify locations of fruit trees in thegeographical region using GPS data.
 8. The system according to claim 5,wherein a plurality of scenes comprises a plurality of images of atleast one of trees, decumbent plants, bushes, groves, plots and fieldsin the geographical region, and wherein the processor is configured touse the information from the image data of the plurality of scenes so asto estimate a number of fruits on the plants in the geographical region.9. The system according to claim 1, wherein the processor is configuredto estimate a number of fruits in the identified regions which includean overlap between two or more fruits in a fruit cluster using a deeplearning module.
 10. The system according to claim 1, wherein thegenerated output with information related to the identified presence offruits is stored in a storage device on a remote server.
 11. The systemaccording to claim 1, further comprising a database for storing theoutput with the information related to the identified presence offruits.
 12. A computer program product comprising a non-transitorycomputer-readable storage medium having program code embodied therewith,the program code executable by at least one hardware processor to:acquire image data of a scene at at least two distinct wavelengths,wherein said at least two distinct wavelengths comprise (a) 970nanometers (nm)±15 nm, and (b) 810 nm±15 nm or 835 nm±15 nm; calculate anormalized difference reflectivity index (NDRI) for each location in anarray of locations in the image data with respect to said at least twodistinct wavelengths, to identify regions in the array of locationswhere the value of the calculated NDRI of the locations in these regionsis within a range of values indicative of a presence of fruits in thescene, and generate an output on an output device with informationrelated to the identified presence of fruits.
 13. The computer programproduct according to claim 12, wherein the scene comprises a plant or apart of a plant.
 14. The computer program product according to claim 12,wherein said program code is executable to acquire said image data fromat least one image sensor configured to acquire said image data of ascene at said at least two distinct wavelengths.
 15. The computerprogram product according to claim 12, wherein said program code isexecutable to estimate a distribution of fruit sizes in the scene usinga deep learning module.
 16. The computer program product according toclaim 12, wherein said image data comprises image data of a plurality ofscenes of the geographical region.
 17. The computer program productaccording to claim 12, wherein the plurality of scenes comprises aplurality of images of fruit trees in the geographical region, and saidprogram code is executable to use the information from the image data ofthe plurality of scenes to estimate a number of fruits in the fruittrees in the geographical region.
 18. The computer program productaccording to claim 12, wherein said program code is executable toidentify locations of the fruit trees in the geographical region basedon global positioning system (GPS) data.
 19. The computer programproduct according to claim 12, wherein said program code is executableto estimate a number of fruits in the identified regions which includean overlap between two or more fruits in a fruit cluster using a deeplearning module.
 20. The computer program product according to claim 12,wherein said program code is executable to generate output withinformation related to the identified presence of fruits and stores saidinformation in a storage device on a remote server.
 21. The computerprogram product according to claim 14, wherein said program code isexecutable to automatically acquire said image data from said imagesensor, count fruit on identified fruit plants and map the number offruit on the fruit plants over a geographical region.